The Place of Interoception and Theory of Mind in Deception

A Subtitle

Introduction

Packages & Data

Packages

This document was prepared on 2021-11-11.

library(tidyverse)
library(patchwork)
library(glmmTMB)
library(report)
library(parameters)
library(correlation)
library(modelbased)
library(performance)
library(see)

summary(report::report(sessionInfo()))

The analysis was done using the R Statistical language (v4.1.1; R Core Team, 2021) on Windows 10 x64, using the packages effectsize (v0.4.5.4), ggplot2 (v3.3.5), stringr (v1.4.0), forcats (v0.5.1), tidyr (v1.1.4), readr (v2.0.2), dplyr (v1.0.7), tibble (v3.1.4), purrr (v0.3.4), parameters (v0.15.0.1), insight (v0.14.5.1), performance (v0.7.3.5), see (v0.7.0.1), easystats (v0.4.3), correlation (v0.7.1.1), modelbased (v0.7.0.1), bayestestR (v0.11.5), report (v0.4.0), datawizard (v0.2.1.9000), glmmTMB (v1.1.2.3), patchwork (v1.1.1) and tidyverse (v1.3.1).

Data

# setwd("C:/Users/user/Desktop/Sputnik/2019-23/DeceptionInteroTom")

df <- read.csv("data/data_combined.csv") %>% 
  mutate(ID = as.factor(paste0("S", ID)),
         condition = as.factor(condition),
         item = as.factor(item),
         style = as.factor(style),
         instruction = as.factor(instruction)) |> 
  #TODO: This renaming should be done at the preprocessing stage
  rename("Participant" = "ID",
         "Condition" = "condition",
         "Item" = "item",
         "Phrasing" = "style",
         "Answer" = "instruction",
         "YONI_Total" = "yoni_total",
         "YONI_Affective" = "yoni_affective",
         "YONI_Cognitive" = "yoni_cognitive",
         "YONI_Physical" = "yoni_physical",
         "BES_Total" = "BES_total",
         "BES_Cognitive" = "BES_cognitive",
         "BES_Affective" = "BES_affective",
         "HCT_Confidence" = "HCT_confidence",
         "HCT_Accuracy" = "HCT_accuracy",
         "HCT_Awareness" = "HCT_awareness",
         "MAIA_Total" = "MAIA_total",
         "MAIA_AttentionRegulation" = "MAIA_attention_regulation",
         "MAIA_BodyListening" = "MAIA_body_listening",
         "MAIA_EmotionalAwareness" = "MAIA_emotional_awareness",
         "MAIA_NotDistracting" = "MAIA_not_distracting",
         "MAIA_NotWorrying" = "MAIA_not_worrying",
         "MAIA_Noticing" = "MAIA_noticing",
         "MAIA_SelfRegulation" = "MAIA_self_regulation",
         "MAIA_Trusting" = "MAIA_trusting",
         "LIE_Ability" = "lie_ability",
         "LIE_Frequency" = "lie_frequency",
         "LIE_Negativity" = "lie_negativity",
         "LIE_Contextuality" = "lie_contextuality",
         "Confidence" = "DT_confidence",
         "RT" = "DT_RT") |> 
  mutate(Answer = fct_recode(Answer, Lie = "LIE", Truth = "TRUTH")) |> 
  select(-HCT_guess, -HCT_noguess, -HCT_onebreath)

cat(paste("The data consists of",
          report::report_participants(df,
                                      participants = "Participant",
                                      sex = "Gender",
                                      age = "Age")))

The data consists of 30 participants (Mean age = 21.1, SD = 2.1, range: [18, 25]; 63.3% females)

Measures

Theory of Mind / Empathy

Yoni Task

df %>% 
  group_by(Participant) %>% 
  select(starts_with("YONI_")) |> 
  summarise_all(mean, na.rm=TRUE) |> 
  tidyr::pivot_longer(-Participant, values_to = "Scores") |> 
  mutate(name = paste0(str_replace(name, "_", " ("), ")")) |> 
  ggplot(aes(x = Scores, fill = name)) +
  geom_density() +
  scale_fill_manual(values = c("YONI (Affective)" = "Purple",
                                 "YONI (Cognitive)" = "Blue",
                                 "YONI (Physical)" = "Green",
                                 "YONI (Total)"= "DarkBlue"),
                      guide = "none") +
  facet_wrap(~name, scales = "free")

BES Questionnaire

df %>% 
  group_by(Participant) %>% 
  select(starts_with("BES_")) |> 
  summarise_all(mean, na.rm=TRUE) |> 
  tidyr::pivot_longer(-Participant, values_to = "Scores") |> 
  mutate(name = paste0(str_replace(name, "_", " ("), ")")) |> 
  ggplot(aes(x = Scores, fill = name)) +
  geom_density() +
  scale_fill_manual(values = c("BES (Affective)" = "Purple",
                               "BES (Cognitive)" = "Blue",
                               "BES (Total)"= "DarkBlue"),
                      guide = "none") +
  facet_wrap(~name, scales = "free")

Interoception

Heartbeat Counting Task (HCT)

df %>% 
  group_by(Participant) %>% 
  select(starts_with("HCT_")) |> 
  summarise_all(mean, na.rm=TRUE) |> 
  tidyr::pivot_longer(-Participant, values_to = "Scores") |> 
  mutate(name = paste0(str_replace(name, "_", " ("), ")")) |> 
  ggplot(aes(x = Scores, fill = name)) +
  geom_density() +
  scale_fill_manual(values = c("HCT (Accuracy)" = "Red",
                               "HCT (Awareness)" = "Orange",
                               "HCT (Confidence)"= "DarkOrange"),
                      guide = "none") +
  facet_wrap(~name, scales = "free")

> Warning: Removed 6 rows containing non-finite values (stat_density).

MAIA

df %>% 
  group_by(Participant) %>% 
  select(starts_with("MAIA_")) |> 
  summarise_all(mean, na.rm=TRUE) |> 
  tidyr::pivot_longer(-Participant, values_to = "Scores") |> 
  mutate(name = paste0(str_replace(name, "_", " ("), ")")) |> 
  ggplot(aes(x = Scores, fill = name)) +
  geom_density() +
  scale_fill_brewer(palette = "Reds", guide = "none") +
  facet_wrap(~name, scales = "free")

Deception

LIE Scale

df %>% 
  group_by(Participant) %>% 
  select(starts_with("LIE_")) |> 
  summarise_all(mean, na.rm=TRUE) |> 
  tidyr::pivot_longer(-Participant, values_to = "Scores") |> 
  mutate(name = paste0(str_replace(name, "_", " ("), ")")) |> 
  ggplot(aes(x = Scores, fill = name)) +
  geom_density() +
  scale_fill_manual(values = c("LIE (Ability)" = "#2196F3",
                               "LIE (Frequency)" = "#4CAF50",
                               "LIE (Contextuality)"= "#FF9800",
                               "LIE (Negativity)"= "#E91E63"),
                    guide = "none") +
  facet_wrap(~name, scales = "free")

Deception Task

Summary Table

df |> 
  group_by(Participant, Answer) |> 
  summarise(Confidence = paste(insight::format_value(mean(Confidence, na.rm = TRUE)),
                               " +- ",
                               insight::format_value(sd(Confidence, na.rm = TRUE))),
            RT = paste(insight::format_value(mean(RT, na.rm = TRUE)),
                       " +- ",
                       insight::format_value(sd(RT, na.rm = TRUE)))) |> 
  arrange(Participant) |> 
  knitr::kable()

> `summarise()` has grouped output by 'Participant'. You can override using the `.groups` argument.

Participant	Answer	Confidence	RT
S1	Lie	0.40 +- 0.10	3.69 +- 0.69
S1	Truth	0.52 +- 0.15	3.75 +- 0.79
S10	Lie	0.59 +- 0.28	3.20 +- 0.71
S10	Truth	0.84 +- 0.13	3.22 +- 0.62
S11	Lie	0.38 +- 0.31	3.60 +- 0.69
S11	Truth	0.72 +- 0.26	3.48 +- 0.62
S12	Lie	0.58 +- 0.13	3.77 +- 1.42
S12	Truth	0.64 +- 0.16	4.40 +- 1.72
S13	Lie	0.28 +- 0.25	7.22 +- 0.82
S13	Truth	0.85 +- 0.17	7.57 +- 0.81
S14	Lie	0.52 +- 0.29	4.13 +- 0.86
S14	Truth	0.62 +- 0.26	5.29 +- 1.54
S15	Lie	+-	4.38 +- 0.94
S15	Truth	+-	4.15 +- 1.04
S16	Lie	0.41 +- 0.18	4.94 +- 1.13
S16	Truth	0.66 +- 0.11	5.07 +- 1.01
S17	Lie	0.63 +- 0.31	2.72 +- 0.63
S17	Truth	0.77 +- 0.19	2.72 +- 0.51
S18	Lie	0.21 +- 0.33	5.88 +- 2.54
S18	Truth	0.79 +- 0.36	5.13 +- 1.98
S19	Lie	+-	3.44 +- 1.55
S19	Truth	+-	3.71 +- 2.05
S2	Lie	0.10 +- 0.17	6.46 +- 1.95
S2	Truth	0.94 +- 0.08	6.83 +- 1.71
S20	Lie	0.37 +- 0.32	3.79 +- 0.74
S20	Truth	0.80 +- 0.17	4.04 +- 1.02
S21	Lie	0.55 +- 0.12	4.97 +- 1.01
S21	Truth	0.74 +- 0.18	5.09 +- 1.04
S22	Lie	0.13 +- 0.29	4.78 +- 2.94
S22	Truth	0.81 +- 0.36	5.12 +- 2.99
S23	Lie	+-	3.10 +- 1.18
S23	Truth	+-	2.99 +- 0.80
S24	Lie	0.27 +- 0.22	2.81 +- 0.68
S24	Truth	0.72 +- 0.17	2.69 +- 0.64
S25	Lie	0.63 +- 0.33	3.71 +- 1.11
S25	Truth	0.85 +- 0.22	3.71 +- 0.92
S26	Lie	0.46 +- 0.21	3.32 +- 0.61
S26	Truth	0.70 +- 0.16	3.23 +- 0.53
S27	Lie	0.32 +- 0.09	5.19 +- 1.88
S27	Truth	0.68 +- 0.07	4.75 +- 1.78
S28	Lie	0.49 +- 0.36	3.78 +- 0.83
S28	Truth	0.59 +- 0.32	3.69 +- 0.50
S29	Lie	0.58 +- 0.50	3.22 +- 0.56
S29	Truth	0.90 +- 0.30	3.31 +- 0.68
S3	Lie	+-	3.35 +- 0.80
S3	Truth	+-	3.42 +- 0.96
S30	Lie	0.80 +- 0.28	4.09 +- 1.10
S30	Truth	0.88 +- 0.18	3.80 +- 0.99
S4	Lie	0.67 +- 0.18	3.86 +- 0.89
S4	Truth	0.77 +- 0.13	3.81 +- 0.80
S5	Lie	0.53 +- 0.23	3.41 +- 0.63
S5	Truth	0.72 +- 0.21	3.45 +- 0.69
S6	Lie	0.11 +- 0.15	3.10 +- 0.52
S6	Truth	0.90 +- 0.09	3.24 +- 0.64
S7	Lie	0.62 +- 0.33	3.84 +- 0.58
S7	Truth	0.69 +- 0.29	3.69 +- 0.55
S8	Lie	0.44 +- 0.17	4.48 +- 0.95
S8	Truth	0.71 +- 0.16	4.59 +- 1.12
S9	Lie	5.79e-04 +- 9.06e-04	4.75 +- 1.42
S9	Truth	1.00 +- 1.18e-03	5.15 +- 1.88

Outliers

df <- df |> 
  dplyr::filter(Participant != "S9",  # Extreme answers
                !Participant %in% c("S3", "S15", "S19", "S23"))  # No data

Distributions

p1 <- df |> 
  dplyr::filter(!Participant %in% c("S29")) |>
  ggplot(aes(x = Confidence, fill = Participant)) +
  geom_density(alpha = 0.1) +
  see::scale_fill_material_d(palette = "rainbow", guide = "none") +
  see::theme_modern() +
  scale_x_continuous(labels = scales::percent, expand=expansion(c(0, .05))) +
  scale_y_continuous(expand=expansion(c(0, .05))) +
  facet_wrap(~Answer)
p2 <- df |> 
  dplyr::filter(!Participant %in% c("S29")) |> 
  ggplot(aes(x = RT, fill = Participant)) +
  geom_density(alpha = 0.1) +
  see::scale_fill_material_d(palette = "rainbow", guide = "none") +
  scale_x_continuous(expand=expansion(c(0, .05))) +
  scale_y_continuous(expand=expansion(c(0, .05))) +
  facet_wrap(~Answer)
p1 / p2

Manipulation Checks

Correlation

dfsub <- df |> 
  select(Participant, 
         starts_with("YONI_"), 
         starts_with("BES_"),
         starts_with("HCT_"),
         starts_with("MAIA_"),
         starts_with("LIE_")) |> 
  group_by(Participant) |> 
  summarise_all(mean)

Theory of Mind / Empathy

r <- correlation(select(dfsub, starts_with("YONI_")),
                 select(dfsub, starts_with("BES_")), 
                 p_adjust = "none")

summary(r) |> 
  plot()

Interoception

r <- correlation(select(dfsub, starts_with("MAIA_")),
                 select(dfsub, starts_with("HCT_")), 
                 p_adjust = "none")

summary(r) |> 
  plot()

ToM and Interoception

r <- correlation(select(dfsub, starts_with(c("MAIA_", "HCT_"))),
                 select(dfsub, starts_with(c("YONI_", "BES_"))), 
                 p_adjust = "none")

summary(r) |> 
  plot()

Phrasing

• Main effect of phrasing on RT only. Indirect questions lead to slower answers.

RT

model <- glmmTMB(RT ~ Answer * Phrasing + (1|Participant) + (1|Item), data = df) 

parameters::parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.01	0.24	0.95	3.54	4.47	16.80	Inf	0.00	fixed
AnswerTruth	0.03	0.07	0.95	-0.11	0.17	0.39	Inf	0.69	fixed
PhrasingIndirect	0.33	0.07	0.95	0.19	0.48	4.58	Inf	0.00	fixed
AnswerTruth:PhrasingIndirect	0.08	0.10	0.95	-0.12	0.28	0.79	Inf	0.43	fixed

estimate_means(model, at = c("Answer", "Phrasing")) |> 
  plot(show_data = "none") 

Confidence

model <- glmmTMB(Confidence ~ Answer * Phrasing + (1|Participant) + (1|Item), data = df) 

parameters::parameters(model)

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects	Group	Component
(Intercept)	0.45	0.02	0.95	0.41	0.49	20.53	Inf	0.00	fixed		conditional
AnswerTruth	0.31	0.02	0.95	0.28	0.34	18.53	Inf	0.00	fixed		conditional
PhrasingIndirect	-0.02	0.02	0.95	-0.05	0.02	-0.93	Inf	0.35	fixed		conditional
AnswerTruth:PhrasingIndirect	0.00	0.02	0.95	-0.04	0.05	0.13	Inf	0.90	fixed		conditional
SD (Intercept)	0.09		0.95	0.07	0.13				random	Participant	conditional
SD (Intercept)	0.01		0.95	0.07	0.13				random	Item	conditional
SD (Observations)	0.26		0.95	0.25	0.27				random	Residual	conditional

estimate_means(model, at = c("Answer", "Phrasing")) |> 
  plot(show_data = "none") 

Lies and RT

• The faster they answer, the more confident they are in their lies.

# Adjustments for beta models
df$Confidence[df$Confidence == 1] <- 0.99999
df$Confidence[df$Confidence == 0] <- 0.00001

model <- glmmTMB(Confidence ~ RT * Answer + Phrasing + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

parameters::parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.77	0.13	0.95	0.51	1.04	5.80	Inf	0.00	fixed
RT	-0.25	0.03	0.95	-0.30	-0.20	-9.53	Inf	0.00	fixed
AnswerTruth	-0.02	0.15	0.95	-0.31	0.26	-0.16	Inf	0.88	fixed
PhrasingIndirect	0.00	0.05	0.95	-0.11	0.10	-0.02	Inf	0.98	fixed
RT:AnswerTruth	0.29	0.03	0.95	0.23	0.35	9.03	Inf	0.00	fixed

estimate_relation(model, at = c("RT", "Answer")) |> 
  plot(length = 50, point = list(alpha = 0.3, size = 3.5)) 

Effect of Condition

Confidence

• Significant interaction between the condition and the answer: the effect of answer (being more confident in truths than in lies) is lower in the social condition.
• Effect mostly driven by the social condition that increases the confidence in lies.
• In other words: harder to lie in the polygraph condition: less obvious feedback cues?

model <- glmmTMB(Confidence ~ Answer * Condition + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

parameters::parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	-0.34	0.08	0.95	-0.51	-0.18	-4.1	Inf	0.00	fixed
AnswerTruth	1.29	0.08	0.95	1.14	1.44	16.4	Inf	0.00	fixed
ConditionSocial	0.14	0.08	0.95	-0.01	0.29	1.8	Inf	0.07	fixed
AnswerTruth:ConditionSocial	-0.22	0.11	0.95	-0.43	-0.01	-2.1	Inf	0.04	fixed

estimate_means(model, at = c("Condition", "Answer")) |> 
  plot(show_data = "none") 

RT

• Significant main effect of the condition: people are slower in the polygraph condition.
• Consistent with the “less intuitive cues.”

model <- glmmTMB(RT ~ Answer * Condition + (1|Participant) + (1|Item), 
                 data = df)

parameters::parameters(model, effects = "fixed")

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.42	0.24	0.95	3.96	4.89	18.57	Inf	0.00	fixed
AnswerTruth	0.07	0.07	0.95	-0.07	0.21	1.00	Inf	0.32	fixed
ConditionSocial	-0.51	0.07	0.95	-0.65	-0.37	-7.05	Inf	0.00	fixed
AnswerTruth:ConditionSocial	-0.01	0.10	0.95	-0.20	0.19	-0.05	Inf	0.96	fixed

estimate_means(model, at = c("Condition", "Answer")) |> 
  plot(show_data = "none") 

Theory of Mind / Empathy

Yoni Task

• When instructed to lie, the decrease in confidence in participants with higher yoni score is less in social than polygraph condition.

Correlation with LIE Scale

get_correlation <- function(var = "YONI_", var2 = "LIE_") {
  r <- correlation(select(dfsub, starts_with(var2)),
                 select(dfsub, starts_with(var)), 
                 p_adjust = "none") |> 
  mutate(Parameter1 = paste0(str_replace(Parameter1, "_", " ("), ")"),
         Parameter2 = paste0(str_replace(Parameter2, "_", " ("), ")"))

  p <- summary(r) |> 
    plot() +
    theme_minimal()
  list(r = r, plot = p)
}

r <- get_correlation(var = "YONI_")
r$plot

Total Score

Confidence

model <- glmmTMB(Confidence ~ Answer / (Condition * YONI_Total) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

get_parameters <- function(model) {
  # Parameters
  params <- parameters::parameters(model, effects = "fixed") 
  # Marginal effects
  at <- c("Answer", "Condition")
  trend <- insight::find_predictors(model)$conditional
  trend <- trend[!trend %in% at]
  marg <- modelbased::estimate_slopes(model, trend = trend, at = at)
  # Output
  list(params = params, marginal_effects = marg)
}

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	2.70	0.95	0.95	0.84	4.56	2.8	Inf	0.00	fixed
AnswerTruth	-3.67	0.89	0.95	-5.41	-1.92	-4.1	Inf	0.00	fixed
AnswerLie:ConditionSocial	-2.47	0.90	0.95	-4.23	-0.71	-2.8	Inf	0.01	fixed
AnswerTruth:ConditionSocial	2.06	0.88	0.95	0.35	3.78	2.4	Inf	0.02	fixed
AnswerLie:YONI_Total	-0.04	0.01	0.95	-0.06	-0.01	-3.2	Inf	0.00	fixed
AnswerTruth:YONI_Total	0.02	0.01	0.95	0.00	0.04	2.0	Inf	0.04	fixed
AnswerLie:ConditionSocial:YONI_Total	0.03	0.01	0.95	0.01	0.05	2.9	Inf	0.00	fixed
AnswerTruth:ConditionSocial:YONI_Total	-0.03	0.01	0.95	-0.05	-0.01	-2.5	Inf	0.01	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	-0.04	0.01	0.95	-0.06	-0.01	-3.22	1980	0.00
Social	Lie	-0.01	0.01	0.95	-0.03	0.02	-0.46	1980	0.64
Polygraph	Truth	0.02	0.01	0.95	0.00	0.04	2.04	1980	0.04
Social	Truth	0.00	0.01	0.95	-0.02	0.02	-0.24	1980	0.81

plot_effect <- function(model, var = "YONI_Total", outcome = "Confidence") {
  data <- df |> 
    group_by(Participant, Answer, Condition) |> 
    summarise({{var}} := mean(.data[[var]], na.rm = TRUE),
              SD = sd(.data[[outcome]], na.rm = TRUE),
              {{outcome}} := mean(.data[[outcome]], na.rm = TRUE),
              CI_low = .data[[outcome]] - SD / 2,
              CI_high = .data[[outcome]] + SD / 2) 
  
  dodge_width <- 0.02 * diff(range(data[[var]]))
  ylab <- ifelse(outcome == "RT", "Reaction Time (s)", "Confidence")
  
  link_data <- estimate_relation(model, at = c("Condition", var, "Answer"), length = 30)
  ggplot(link_data, aes_string(x = var, y = "Predicted")) +
    geom_pointrange(data = data, aes_string(y = outcome, color = "Condition", ymin = "CI_low", ymax = "CI_high"), position = position_dodge(width = dodge_width)) +
    geom_ribbon(aes(ymin = CI_low, ymax = CI_high, fill = Condition), alpha = 0.33) + 
    geom_line(aes(color = Condition)) +
    labs(y = ylab, x = paste0(stringr::str_replace(var, "_", " ("), ")")) +
    scale_color_manual(values = c("Polygraph" = "#FF5722", "Social" = "#2196F3")) +
    scale_fill_manual(values = c("Polygraph" = "#FF5722", "Social" = "#2196F3")) +
    facet_wrap(~Answer) 
}

plot_effect(model, var = "YONI_Total", outcome = "Confidence")

RT

model <- glmmTMB(RT ~ Answer / (Condition * YONI_Total) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	0.60	2.53	0.95	-4.37	5.57	0.24	Inf	0.81	fixed
AnswerTruth	2.81	0.81	0.95	1.23	4.40	3.48	Inf	0.00	fixed
AnswerLie:ConditionSocial	1.35	0.81	0.95	-0.24	2.93	1.67	Inf	0.10	fixed
AnswerTruth:ConditionSocial	-1.49	0.81	0.95	-3.07	0.09	-1.85	Inf	0.06	fixed
AnswerLie:YONI_Total	0.04	0.03	0.95	-0.01	0.10	1.52	Inf	0.13	fixed
AnswerTruth:YONI_Total	0.01	0.03	0.95	-0.05	0.07	0.43	Inf	0.67	fixed
AnswerLie:ConditionSocial:YONI_Total	-0.02	0.01	0.95	-0.04	0.00	-2.30	Inf	0.02	fixed
AnswerTruth:ConditionSocial:YONI_Total	0.01	0.01	0.95	-0.01	0.03	1.22	Inf	0.22	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	0.04	0.03	0.95	-0.01	0.10	1.52	1989	0.13
Social	Lie	0.02	0.03	0.95	-0.03	0.08	0.78	1989	0.44
Polygraph	Truth	0.01	0.03	0.95	-0.05	0.07	0.43	1989	0.67
Social	Truth	0.02	0.03	0.95	-0.03	0.08	0.82	1989	0.41

plot_effect(model, var = "YONI_Total", outcome = "RT")

BES

Total Score

LIE Scale

r <- get_correlation(var = "BES_")
r$plot

Confidence

model <- glmmTMB(Confidence ~ Answer / (Condition * BES_Total) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	2.29	0.74	0.95	0.84	3.73	3.1	Inf	0.00	fixed
AnswerTruth	-3.03	0.66	0.95	-4.31	-1.74	-4.6	Inf	0.00	fixed
AnswerLie:ConditionSocial	-1.58	0.67	0.95	-2.91	-0.26	-2.4	Inf	0.02	fixed
AnswerTruth:ConditionSocial	1.00	0.67	0.95	-0.30	2.31	1.5	Inf	0.13	fixed
AnswerLie:BES_Total	-0.03	0.01	0.95	-0.05	-0.02	-3.6	Inf	0.00	fixed
AnswerTruth:BES_Total	0.02	0.01	0.95	0.00	0.04	2.3	Inf	0.02	fixed
AnswerLie:ConditionSocial:BES_Total	0.02	0.01	0.95	0.01	0.04	2.5	Inf	0.01	fixed
AnswerTruth:ConditionSocial:BES_Total	-0.01	0.01	0.95	-0.03	0.00	-1.6	Inf	0.10	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	-0.03	0.01	0.95	-0.05	-0.02	-3.57	1980	0.00
Social	Lie	-0.01	0.01	0.95	-0.03	0.01	-1.21	1980	0.22
Polygraph	Truth	0.02	0.01	0.95	0.00	0.04	2.28	1980	0.02
Social	Truth	0.01	0.01	0.95	-0.01	0.03	0.82	1980	0.41

plot_effect(model, var = "BES_Total", outcome = "Confidence")

RT

model <- glmmTMB(RT ~ Answer / (Condition * BES_Total) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.80	2.08	0.95	0.73	8.87	2.31	Inf	0.02	fixed
AnswerTruth	1.96	0.66	0.95	0.66	3.25	2.97	Inf	0.00	fixed
AnswerLie:ConditionSocial	1.27	0.66	0.95	-0.02	2.56	1.93	Inf	0.05	fixed
AnswerTruth:ConditionSocial	-1.16	0.66	0.95	-2.45	0.13	-1.76	Inf	0.08	fixed
AnswerLie:BES_Total	-0.01	0.03	0.95	-0.06	0.05	-0.18	Inf	0.85	fixed
AnswerTruth:BES_Total	-0.03	0.03	0.95	-0.08	0.02	-1.10	Inf	0.27	fixed
AnswerLie:ConditionSocial:BES_Total	-0.02	0.01	0.95	-0.04	-0.01	-2.72	Inf	0.01	fixed
AnswerTruth:ConditionSocial:BES_Total	0.01	0.01	0.95	-0.01	0.03	0.99	Inf	0.32	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	-0.01	0.03	0.95	-0.06	0.05	-0.18	1989	0.85
Social	Lie	-0.03	0.03	0.95	-0.08	0.03	-1.05	1989	0.30
Polygraph	Truth	-0.03	0.03	0.95	-0.08	0.02	-1.10	1989	0.27
Social	Truth	-0.02	0.03	0.95	-0.08	0.03	-0.78	1989	0.43

plot_effect(model, var = "BES_Total", outcome = "RT")

Interoception

Heartbeat Counting

Correlation with LIE Scale

r <- get_correlation(var = "HCT_")
r$plot

# plot(correlation::cor_test(dfsub, "HCT_Accuracy", "LIE_Ability"))

Accuracy

Confidence

model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Accuracy) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	-1.17	0.26	0.95	-1.68	-0.67	-4.6	Inf	0.00	fixed
AnswerTruth	2.76	0.23	0.95	2.30	3.22	11.9	Inf	0.00	fixed
AnswerLie:ConditionSocial	0.62	0.23	0.95	0.16	1.08	2.7	Inf	0.01	fixed
AnswerTruth:ConditionSocial	-0.59	0.23	0.95	-1.04	-0.14	-2.6	Inf	0.01	fixed
AnswerLie:HCT_Accuracy	1.37	0.40	0.95	0.59	2.16	3.4	Inf	0.00	fixed
AnswerTruth:HCT_Accuracy	-1.06	0.40	0.95	-1.84	-0.28	-2.7	Inf	0.01	fixed
AnswerLie:ConditionSocial:HCT_Accuracy	-0.81	0.36	0.95	-1.52	-0.09	-2.2	Inf	0.03	fixed
AnswerTruth:ConditionSocial:HCT_Accuracy	0.85	0.36	0.95	0.15	1.55	2.4	Inf	0.02	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	1.37	0.4	0.95	0.59	2.16	3.44	1980	0.00
Social	Lie	0.57	0.4	0.95	-0.22	1.35	1.42	1980	0.15
Polygraph	Truth	-1.06	0.4	0.95	-1.84	-0.28	-2.68	1980	0.01
Social	Truth	-0.21	0.4	0.95	-0.99	0.57	-0.53	1980	0.59

plot_effect(model, var = "HCT_Accuracy", outcome = "Confidence")

RT

model <- glmmTMB(RT ~ Answer / (Condition * HCT_Accuracy) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.87	0.70	0.95	3.49	6.25	6.92	Inf	0.00	fixed
AnswerTruth	-0.30	0.22	0.95	-0.74	0.13	-1.37	Inf	0.17	fixed
AnswerLie:ConditionSocial	-0.51	0.22	0.95	-0.94	-0.07	-2.28	Inf	0.02	fixed
AnswerTruth:ConditionSocial	-0.30	0.22	0.95	-0.73	0.13	-1.36	Inf	0.17	fixed
AnswerLie:HCT_Accuracy	-0.74	1.09	0.95	-2.88	1.41	-0.67	Inf	0.50	fixed
AnswerTruth:HCT_Accuracy	-0.12	1.09	0.95	-2.26	2.02	-0.11	Inf	0.92	fixed
AnswerLie:ConditionSocial:HCT_Accuracy	0.00	0.34	0.95	-0.68	0.67	-0.01	Inf	1.00	fixed
AnswerTruth:ConditionSocial:HCT_Accuracy	-0.35	0.34	0.95	-1.02	0.33	-1.01	Inf	0.31	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	-0.74	1.1	0.95	-2.9	1.4	-0.67	1989	0.50
Social	Lie	-0.74	1.1	0.95	-2.9	1.4	-0.68	1989	0.50
Polygraph	Truth	-0.12	1.1	0.95	-2.3	2.0	-0.11	1989	0.92
Social	Truth	-0.47	1.1	0.95	-2.6	1.7	-0.43	1989	0.67

plot_effect(model, var = "HCT_Accuracy", outcome = "RT")

Interoceptive Confidence

Confidence

model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Confidence) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	-0.90	0.17	0.95	-1.23	-0.58	-5.39	Inf	0.00	fixed
AnswerTruth	1.88	0.16	0.95	1.57	2.20	11.60	Inf	0.00	fixed
AnswerLie:ConditionSocial	0.45	0.16	0.95	0.13	0.77	2.75	Inf	0.01	fixed
AnswerTruth:ConditionSocial	-0.40	0.16	0.95	-0.71	-0.08	-2.48	Inf	0.01	fixed
AnswerLie:HCT_Confidence	1.11	0.29	0.95	0.54	1.69	3.80	Inf	0.00	fixed
AnswerTruth:HCT_Confidence	-0.07	0.29	0.95	-0.64	0.50	-0.25	Inf	0.80	fixed
AnswerLie:ConditionSocial:HCT_Confidence	-0.63	0.29	0.95	-1.19	-0.06	-2.17	Inf	0.03	fixed
AnswerTruth:ConditionSocial:HCT_Confidence	0.63	0.28	0.95	0.08	1.19	2.24	Inf	0.03	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	1.11	0.29	0.95	0.54	1.7	3.80	1980	0.00
Social	Lie	0.49	0.30	0.95	-0.10	1.1	1.64	1980	0.10
Polygraph	Truth	-0.07	0.29	0.95	-0.64	0.5	-0.25	1980	0.80
Social	Truth	0.56	0.29	0.95	-0.02	1.1	1.91	1980	0.06

plot_effect(model, var = "HCT_Confidence", outcome = "Confidence")

RT

model <- glmmTMB(RT ~ Answer / (Condition * HCT_Confidence) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	5.41	0.45	0.95	4.53	6.28	12.08	Inf	0.00	fixed
AnswerTruth	0.03	0.15	0.95	-0.28	0.33	0.17	Inf	0.87	fixed
AnswerLie:ConditionSocial	-0.71	0.15	0.95	-1.01	-0.41	-4.64	Inf	0.00	fixed
AnswerTruth:ConditionSocial	-0.47	0.15	0.95	-0.77	-0.17	-3.08	Inf	0.00	fixed
AnswerLie:HCT_Confidence	-1.96	0.78	0.95	-3.49	-0.43	-2.51	Inf	0.01	fixed
AnswerTruth:HCT_Confidence	-1.86	0.78	0.95	-3.40	-0.33	-2.39	Inf	0.02	fixed
AnswerLie:ConditionSocial:HCT_Confidence	0.41	0.27	0.95	-0.12	0.94	1.52	Inf	0.13	fixed
AnswerTruth:ConditionSocial:HCT_Confidence	-0.08	0.27	0.95	-0.61	0.45	-0.29	Inf	0.77	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	-2.0	0.78	0.95	-3.5	-0.43	-2.5	1989	0.01
Social	Lie	-1.6	0.78	0.95	-3.1	-0.01	-2.0	1989	0.05
Polygraph	Truth	-1.9	0.78	0.95	-3.4	-0.33	-2.4	1989	0.02
Social	Truth	-1.9	0.78	0.95	-3.5	-0.41	-2.5	1989	0.01

plot_effect(model, var = "HCT_Confidence", outcome = "RT")

Interoceptive Awareness

Confidence

model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Awareness) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	-0.35	0.08	0.95	-0.52	-0.19	-4.15	Inf	0.00	fixed
AnswerTruth	1.31	0.08	0.95	1.16	1.46	16.82	Inf	0.00	fixed
AnswerLie:ConditionSocial	0.14	0.08	0.95	-0.01	0.29	1.83	Inf	0.07	fixed
AnswerTruth:ConditionSocial	-0.09	0.08	0.95	-0.24	0.06	-1.14	Inf	0.25	fixed
AnswerLie:HCT_Awareness	-0.60	0.14	0.95	-0.87	-0.33	-4.35	Inf	0.00	fixed
AnswerTruth:HCT_Awareness	0.19	0.14	0.95	-0.08	0.46	1.40	Inf	0.16	fixed
AnswerLie:ConditionSocial:HCT_Awareness	0.58	0.13	0.95	0.33	0.83	4.52	Inf	0.00	fixed
AnswerTruth:ConditionSocial:HCT_Awareness	0.00	0.13	0.95	-0.25	0.24	-0.03	Inf	0.98	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	-0.60	0.14	0.95	-0.87	-0.33	-4.35	1980	0.00
Social	Lie	-0.03	0.14	0.95	-0.30	0.25	-0.19	1980	0.85
Polygraph	Truth	0.19	0.14	0.95	-0.08	0.46	1.40	1980	0.16
Social	Truth	0.19	0.14	0.95	-0.08	0.46	1.37	1980	0.17

plot_effect(model, var = "HCT_Awareness", outcome = "Confidence")

RT

model <- glmmTMB(RT ~ Answer / (Condition * HCT_Awareness) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.45	0.22	0.95	4.01	4.88	19.96	Inf	0.00	fixed
AnswerTruth	0.07	0.07	0.95	-0.07	0.21	0.93	Inf	0.35	fixed
AnswerLie:ConditionSocial	-0.51	0.07	0.95	-0.65	-0.37	-7.16	Inf	0.00	fixed
AnswerTruth:ConditionSocial	-0.51	0.07	0.95	-0.65	-0.37	-7.16	Inf	0.00	fixed
AnswerLie:HCT_Awareness	0.92	0.35	0.95	0.22	1.61	2.58	Inf	0.01	fixed
AnswerTruth:HCT_Awareness	0.70	0.35	0.95	0.01	1.40	1.98	Inf	0.05	fixed
AnswerLie:ConditionSocial:HCT_Awareness	-0.32	0.12	0.95	-0.55	-0.08	-2.66	Inf	0.01	fixed
AnswerTruth:ConditionSocial:HCT_Awareness	-0.08	0.12	0.95	-0.31	0.16	-0.64	Inf	0.52	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	0.92	0.35	0.95	0.22	1.6	2.6	1989	0.01
Social	Lie	0.60	0.35	0.95	-0.10	1.3	1.7	1989	0.09
Polygraph	Truth	0.70	0.35	0.95	0.01	1.4	2.0	1989	0.05
Social	Truth	0.63	0.35	0.95	-0.07	1.3	1.8	1989	0.08

plot_effect(model, var = "HCT_Awareness", outcome = "RT")

MAIA

Correlation with LIE Scale

r <- get_correlation(var = "MAIA_")
r$plot +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

Total Score

Confidence

model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_Total) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	-2.39	0.40	0.95	-3.17	-1.60	-6.0	Inf	0.00	fixed
AnswerTruth	3.93	0.37	0.95	3.20	4.66	10.5	Inf	0.00	fixed
AnswerLie:ConditionSocial	1.48	0.38	0.95	0.73	2.23	3.8	Inf	0.00	fixed
AnswerTruth:ConditionSocial	-0.93	0.38	0.95	-1.67	-0.19	-2.4	Inf	0.01	fixed
AnswerLie:MAIA_Total	0.75	0.14	0.95	0.47	1.04	5.2	Inf	0.00	fixed
AnswerTruth:MAIA_Total	-0.22	0.14	0.95	-0.50	0.06	-1.5	Inf	0.12	fixed
AnswerLie:ConditionSocial:MAIA_Total	-0.50	0.14	0.95	-0.77	-0.23	-3.6	Inf	0.00	fixed
AnswerTruth:ConditionSocial:MAIA_Total	0.31	0.14	0.95	0.05	0.58	2.3	Inf	0.02	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	0.75	0.14	0.95	0.47	1.04	5.23	1980	0.00
Social	Lie	0.26	0.15	0.95	-0.03	0.54	1.76	1980	0.08
Polygraph	Truth	-0.22	0.14	0.95	-0.50	0.06	-1.54	1980	0.12
Social	Truth	0.09	0.15	0.95	-0.19	0.38	0.64	1980	0.52

plot_effect(model, var = "MAIA_Total", outcome = "Confidence")

RT

model <- glmmTMB(RT ~ Answer / (Condition * MAIA_Total) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	7.47	1.07	0.95	5.38	9.56	6.99	Inf	0.00	fixed
AnswerTruth	-0.19	0.36	0.95	-0.90	0.52	-0.53	Inf	0.60	fixed
AnswerLie:ConditionSocial	-2.21	0.36	0.95	-2.92	-1.50	-6.12	Inf	0.00	fixed
AnswerTruth:ConditionSocial	-1.65	0.36	0.95	-2.36	-0.94	-4.57	Inf	0.00	fixed
AnswerLie:MAIA_Total	-1.12	0.38	0.95	-1.87	-0.37	-2.91	Inf	0.00	fixed
AnswerTruth:MAIA_Total	-1.02	0.38	0.95	-1.78	-0.27	-2.66	Inf	0.01	fixed
AnswerLie:ConditionSocial:MAIA_Total	0.63	0.13	0.95	0.37	0.88	4.82	Inf	0.00	fixed
AnswerTruth:ConditionSocial:MAIA_Total	0.42	0.13	0.95	0.16	0.67	3.22	Inf	0.00	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	-1.12	0.38	0.95	-1.9	-0.37	-2.9	1989	0.00
Social	Lie	-0.49	0.38	0.95	-1.2	0.26	-1.3	1989	0.20
Polygraph	Truth	-1.02	0.38	0.95	-1.8	-0.27	-2.7	1989	0.01
Social	Truth	-0.60	0.38	0.95	-1.4	0.15	-1.6	1989	0.12

plot_effect(model, var = "MAIA_Total", outcome = "RT")

Noticing

Confidence

model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_Noticing) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	-1.64	0.37	0.95	-2.37	-0.92	-4.4	Inf	0.00	fixed
AnswerTruth	3.44	0.32	0.95	2.81	4.08	10.7	Inf	0.00	fixed
AnswerLie:ConditionSocial	1.70	0.34	0.95	1.03	2.36	5.0	Inf	0.00	fixed
AnswerTruth:ConditionSocial	-0.57	0.33	0.95	-1.22	0.08	-1.7	Inf	0.09	fixed
AnswerLie:MAIA_Noticing	0.39	0.11	0.95	0.18	0.60	3.6	Inf	0.00	fixed
AnswerTruth:MAIA_Noticing	-0.25	0.11	0.95	-0.47	-0.04	-2.3	Inf	0.02	fixed
AnswerLie:ConditionSocial:MAIA_Noticing	-0.47	0.10	0.95	-0.66	-0.28	-4.8	Inf	0.00	fixed
AnswerTruth:ConditionSocial:MAIA_Noticing	0.15	0.10	0.95	-0.04	0.33	1.5	Inf	0.13	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	0.39	0.11	0.95	0.18	0.60	3.65	1980	0.00
Social	Lie	-0.08	0.11	0.95	-0.30	0.14	-0.70	1980	0.48
Polygraph	Truth	-0.25	0.11	0.95	-0.47	-0.04	-2.34	1980	0.02
Social	Truth	-0.11	0.11	0.95	-0.32	0.11	-0.99	1980	0.32

plot_effect(model, var = "MAIA_Noticing", outcome = "Confidence")

RT

model <- glmmTMB(RT ~ Answer / (Condition * MAIA_Noticing) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	6.72	1.03	0.95	4.70	8.73	6.52	Inf	0.00	fixed
AnswerTruth	-0.98	0.33	0.95	-1.64	-0.33	-2.96	Inf	0.00	fixed
AnswerLie:ConditionSocial	-1.92	0.33	0.95	-2.57	-1.26	-5.76	Inf	0.00	fixed
AnswerTruth:ConditionSocial	-0.75	0.33	0.95	-1.40	-0.10	-2.25	Inf	0.02	fixed
AnswerLie:MAIA_Noticing	-0.68	0.30	0.95	-1.26	-0.10	-2.28	Inf	0.02	fixed
AnswerTruth:MAIA_Noticing	-0.36	0.30	0.95	-0.94	0.22	-1.23	Inf	0.22	fixed
AnswerLie:ConditionSocial:MAIA_Noticing	0.42	0.10	0.95	0.23	0.60	4.34	Inf	0.00	fixed
AnswerTruth:ConditionSocial:MAIA_Noticing	0.07	0.10	0.95	-0.12	0.26	0.73	Inf	0.47	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	-0.68	0.3	0.95	-1.26	-0.10	-2.28	1989	0.02
Social	Lie	-0.26	0.3	0.95	-0.84	0.32	-0.88	1989	0.38
Polygraph	Truth	-0.36	0.3	0.95	-0.94	0.22	-1.23	1989	0.22
Social	Truth	-0.29	0.3	0.95	-0.87	0.29	-0.99	1989	0.32

plot_effect(model, var = "MAIA_Noticing", outcome = "RT")

Not Distracting

Confidence

model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_NotDistracting) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	-0.79	0.17	0.95	-1.12	-0.45	-4.60	Inf	0.00	fixed
AnswerTruth	1.63	0.16	0.95	1.31	1.95	10.02	Inf	0.00	fixed
AnswerLie:ConditionSocial	0.32	0.16	0.95	0.01	0.64	2.00	Inf	0.05	fixed
AnswerTruth:ConditionSocial	0.00	0.16	0.95	-0.31	0.32	0.02	Inf	0.99	fixed
AnswerLie:MAIA_NotDistracting	0.27	0.09	0.95	0.09	0.45	2.95	Inf	0.00	fixed
AnswerTruth:MAIA_NotDistracting	0.06	0.09	0.95	-0.11	0.24	0.70	Inf	0.48	fixed
AnswerLie:ConditionSocial:MAIA_NotDistracting	-0.11	0.09	0.95	-0.28	0.06	-1.30	Inf	0.19	fixed
AnswerTruth:ConditionSocial:MAIA_NotDistracting	-0.05	0.09	0.95	-0.22	0.11	-0.63	Inf	0.53	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	0.27	0.09	0.95	0.09	0.45	2.95	1980	0.00
Social	Lie	0.16	0.09	0.95	-0.02	0.34	1.71	1980	0.09
Polygraph	Truth	0.06	0.09	0.95	-0.11	0.24	0.70	1980	0.48
Social	Truth	0.01	0.09	0.95	-0.17	0.19	0.11	1980	0.92

plot_effect(model, var = "MAIA_NotDistracting", outcome = "Confidence")

RT

model <- glmmTMB(RT ~ Answer / (Condition * MAIA_NotDistracting) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params

Parameter	Coefficient	SE	CI	CI_low	CI_high	z	df_error	p	Effects
(Intercept)	4.60	0.49	0.95	3.64	5.56	9.38	Inf	0.00	fixed
AnswerTruth	-0.07	0.15	0.95	-0.37	0.23	-0.46	Inf	0.65	fixed
AnswerLie:ConditionSocial	-0.49	0.15	0.95	-0.79	-0.19	-3.23	Inf	0.00	fixed
AnswerTruth:ConditionSocial	-0.23	0.15	0.95	-0.53	0.07	-1.49	Inf	0.14	fixed
AnswerLie:MAIA_NotDistracting	-0.11	0.26	0.95	-0.62	0.40	-0.41	Inf	0.68	fixed
AnswerTruth:MAIA_NotDistracting	-0.02	0.26	0.95	-0.53	0.49	-0.08	Inf	0.94	fixed
AnswerLie:ConditionSocial:MAIA_NotDistracting	-0.01	0.08	0.95	-0.17	0.15	-0.09	Inf	0.93	fixed
AnswerTruth:ConditionSocial:MAIA_NotDistracting	-0.17	0.08	0.95	-0.34	-0.01	-2.10	Inf	0.04	fixed

results$marginal_effects

Condition	Answer	Coefficient	SE	CI	CI_low	CI_high	t	df_error	p
Polygraph	Lie	-0.11	0.26	0.95	-0.62	0.41	-0.41	1989	0.68
Social	Lie	-0.12	0.26	0.95	-0.63	0.40	-0.44	1989	0.66
Polygraph	Truth	-0.02	0.26	0.95	-0.53	0.49	-0.08	1989	0.94
Social	Truth	-0.19	0.26	0.95	-0.71	0.32	-0.74	1989	0.46

plot_effect(model, var = "MAIA_NotDistracting", outcome = "RT")

Full Code

The full script of executive code contained in this document is reproduced here.

# Set up the environment (or use local alternative `source("utils/config.R")`)
source("https://raw.githubusercontent.com/RealityBending/TemplateResults/main/utils/config.R")  

library(ggplot2)
theme_set(see::theme_modern())
# This chunk is a bit complex so don't worry about it: it's made to add badges to the HTML versions
# NOTE: You have to replace the links accordingly to have working "buttons" on the HTML versions
if (!knitr::is_latex_output() && knitr::is_html_output()) {
  cat("![Build](https://github.com/RealityBending/TemplateResults/workflows/Build/badge.svg)
      [![Website](https://img.shields.io/badge/repo-Readme-2196F3)](https://github.com/RealityBending/TemplateResults)
      [![Website](https://img.shields.io/badge/visit-website-E91E63)](https://realitybending.github.io/TemplateResults/)
      [![Website](https://img.shields.io/badge/download-.docx-FF5722)](https://github.com/RealityBending/TemplateResults/raw/main/word_and_pdf/SupplementaryMaterials.docx)
      [![Website](https://img.shields.io/badge/see-.pdf-FF9800)](https://github.com/RealityBending/TemplateResults/blob/main/word_and_pdf/SupplementaryMaterials.pdf)")
}
library(tidyverse)
library(patchwork)
library(glmmTMB)
library(report)
library(parameters)
library(correlation)
library(modelbased)
library(performance)
library(see)

summary(report::report(sessionInfo()))
# setwd("C:/Users/user/Desktop/Sputnik/2019-23/DeceptionInteroTom")

df <- read.csv("data/data_combined.csv") %>% 
  mutate(ID = as.factor(paste0("S", ID)),
         condition = as.factor(condition),
         item = as.factor(item),
         style = as.factor(style),
         instruction = as.factor(instruction)) |> 
  #TODO: This renaming should be done at the preprocessing stage
  rename("Participant" = "ID",
         "Condition" = "condition",
         "Item" = "item",
         "Phrasing" = "style",
         "Answer" = "instruction",
         "YONI_Total" = "yoni_total",
         "YONI_Affective" = "yoni_affective",
         "YONI_Cognitive" = "yoni_cognitive",
         "YONI_Physical" = "yoni_physical",
         "BES_Total" = "BES_total",
         "BES_Cognitive" = "BES_cognitive",
         "BES_Affective" = "BES_affective",
         "HCT_Confidence" = "HCT_confidence",
         "HCT_Accuracy" = "HCT_accuracy",
         "HCT_Awareness" = "HCT_awareness",
         "MAIA_Total" = "MAIA_total",
         "MAIA_AttentionRegulation" = "MAIA_attention_regulation",
         "MAIA_BodyListening" = "MAIA_body_listening",
         "MAIA_EmotionalAwareness" = "MAIA_emotional_awareness",
         "MAIA_NotDistracting" = "MAIA_not_distracting",
         "MAIA_NotWorrying" = "MAIA_not_worrying",
         "MAIA_Noticing" = "MAIA_noticing",
         "MAIA_SelfRegulation" = "MAIA_self_regulation",
         "MAIA_Trusting" = "MAIA_trusting",
         "LIE_Ability" = "lie_ability",
         "LIE_Frequency" = "lie_frequency",
         "LIE_Negativity" = "lie_negativity",
         "LIE_Contextuality" = "lie_contextuality",
         "Confidence" = "DT_confidence",
         "RT" = "DT_RT") |> 
  mutate(Answer = fct_recode(Answer, Lie = "LIE", Truth = "TRUTH")) |> 
  select(-HCT_guess, -HCT_noguess, -HCT_onebreath)

cat(paste("The data consists of",
          report::report_participants(df,
                                      participants = "Participant",
                                      sex = "Gender",
                                      age = "Age")))
report::cite_packages(sessionInfo())
df %>% 
  group_by(Participant) %>% 
  select(starts_with("YONI_")) |> 
  summarise_all(mean, na.rm=TRUE) |> 
  tidyr::pivot_longer(-Participant, values_to = "Scores") |> 
  mutate(name = paste0(str_replace(name, "_", " ("), ")")) |> 
  ggplot(aes(x = Scores, fill = name)) +
  geom_density() +
  scale_fill_manual(values = c("YONI (Affective)" = "Purple",
                                 "YONI (Cognitive)" = "Blue",
                                 "YONI (Physical)" = "Green",
                                 "YONI (Total)"= "DarkBlue"),
                      guide = "none") +
  facet_wrap(~name, scales = "free")
df %>% 
  group_by(Participant) %>% 
  select(starts_with("BES_")) |> 
  summarise_all(mean, na.rm=TRUE) |> 
  tidyr::pivot_longer(-Participant, values_to = "Scores") |> 
  mutate(name = paste0(str_replace(name, "_", " ("), ")")) |> 
  ggplot(aes(x = Scores, fill = name)) +
  geom_density() +
  scale_fill_manual(values = c("BES (Affective)" = "Purple",
                               "BES (Cognitive)" = "Blue",
                               "BES (Total)"= "DarkBlue"),
                      guide = "none") +
  facet_wrap(~name, scales = "free")
df %>% 
  group_by(Participant) %>% 
  select(starts_with("HCT_")) |> 
  summarise_all(mean, na.rm=TRUE) |> 
  tidyr::pivot_longer(-Participant, values_to = "Scores") |> 
  mutate(name = paste0(str_replace(name, "_", " ("), ")")) |> 
  ggplot(aes(x = Scores, fill = name)) +
  geom_density() +
  scale_fill_manual(values = c("HCT (Accuracy)" = "Red",
                               "HCT (Awareness)" = "Orange",
                               "HCT (Confidence)"= "DarkOrange"),
                      guide = "none") +
  facet_wrap(~name, scales = "free")
df %>% 
  group_by(Participant) %>% 
  select(starts_with("MAIA_")) |> 
  summarise_all(mean, na.rm=TRUE) |> 
  tidyr::pivot_longer(-Participant, values_to = "Scores") |> 
  mutate(name = paste0(str_replace(name, "_", " ("), ")")) |> 
  ggplot(aes(x = Scores, fill = name)) +
  geom_density() +
  scale_fill_brewer(palette = "Reds", guide = "none") +
  facet_wrap(~name, scales = "free")
df %>% 
  group_by(Participant) %>% 
  select(starts_with("LIE_")) |> 
  summarise_all(mean, na.rm=TRUE) |> 
  tidyr::pivot_longer(-Participant, values_to = "Scores") |> 
  mutate(name = paste0(str_replace(name, "_", " ("), ")")) |> 
  ggplot(aes(x = Scores, fill = name)) +
  geom_density() +
  scale_fill_manual(values = c("LIE (Ability)" = "#2196F3",
                               "LIE (Frequency)" = "#4CAF50",
                               "LIE (Contextuality)"= "#FF9800",
                               "LIE (Negativity)"= "#E91E63"),
                    guide = "none") +
  facet_wrap(~name, scales = "free")

df |> 
  group_by(Participant, Answer) |> 
  summarise(Confidence = paste(insight::format_value(mean(Confidence, na.rm = TRUE)),
                               " +- ",
                               insight::format_value(sd(Confidence, na.rm = TRUE))),
            RT = paste(insight::format_value(mean(RT, na.rm = TRUE)),
                       " +- ",
                       insight::format_value(sd(RT, na.rm = TRUE)))) |> 
  arrange(Participant) |> 
  knitr::kable()
df <- df |> 
  dplyr::filter(Participant != "S9",  # Extreme answers
                !Participant %in% c("S3", "S15", "S19", "S23"))  # No data
p1 <- df |> 
  dplyr::filter(!Participant %in% c("S29")) |>
  ggplot(aes(x = Confidence, fill = Participant)) +
  geom_density(alpha = 0.1) +
  see::scale_fill_material_d(palette = "rainbow", guide = "none") +
  see::theme_modern() +
  scale_x_continuous(labels = scales::percent, expand=expansion(c(0, .05))) +
  scale_y_continuous(expand=expansion(c(0, .05))) +
  facet_wrap(~Answer)
p2 <- df |> 
  dplyr::filter(!Participant %in% c("S29")) |> 
  ggplot(aes(x = RT, fill = Participant)) +
  geom_density(alpha = 0.1) +
  see::scale_fill_material_d(palette = "rainbow", guide = "none") +
  scale_x_continuous(expand=expansion(c(0, .05))) +
  scale_y_continuous(expand=expansion(c(0, .05))) +
  facet_wrap(~Answer)
p1 / p2
dfsub <- df |> 
  select(Participant, 
         starts_with("YONI_"), 
         starts_with("BES_"),
         starts_with("HCT_"),
         starts_with("MAIA_"),
         starts_with("LIE_")) |> 
  group_by(Participant) |> 
  summarise_all(mean)
r <- correlation(select(dfsub, starts_with("YONI_")),
                 select(dfsub, starts_with("BES_")), 
                 p_adjust = "none")

summary(r) |> 
  plot()
r <- correlation(select(dfsub, starts_with("MAIA_")),
                 select(dfsub, starts_with("HCT_")), 
                 p_adjust = "none")

summary(r) |> 
  plot()
r <- correlation(select(dfsub, starts_with(c("MAIA_", "HCT_"))),
                 select(dfsub, starts_with(c("YONI_", "BES_"))), 
                 p_adjust = "none")

summary(r) |> 
  plot()
model <- glmmTMB(RT ~ Answer * Phrasing + (1|Participant) + (1|Item), data = df) 

parameters::parameters(model, effects = "fixed")
estimate_means(model, at = c("Answer", "Phrasing")) |> 
  plot(show_data = "none") 
model <- glmmTMB(Confidence ~ Answer * Phrasing + (1|Participant) + (1|Item), data = df) 

parameters::parameters(model)
estimate_means(model, at = c("Answer", "Phrasing")) |> 
  plot(show_data = "none") 
# Adjustments for beta models
df$Confidence[df$Confidence == 1] <- 0.99999
df$Confidence[df$Confidence == 0] <- 0.00001

model <- glmmTMB(Confidence ~ RT * Answer + Phrasing + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

parameters::parameters(model, effects = "fixed")
estimate_relation(model, at = c("RT", "Answer")) |> 
  plot(length = 50, point = list(alpha = 0.3, size = 3.5)) 
model <- glmmTMB(Confidence ~ Answer * Condition + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

parameters::parameters(model, effects = "fixed")
estimate_means(model, at = c("Condition", "Answer")) |> 
  plot(show_data = "none") 
model <- glmmTMB(RT ~ Answer * Condition + (1|Participant) + (1|Item), 
                 data = df)

parameters::parameters(model, effects = "fixed")
estimate_means(model, at = c("Condition", "Answer")) |> 
  plot(show_data = "none") 
get_correlation <- function(var = "YONI_", var2 = "LIE_") {
  r <- correlation(select(dfsub, starts_with(var2)),
                 select(dfsub, starts_with(var)), 
                 p_adjust = "none") |> 
  mutate(Parameter1 = paste0(str_replace(Parameter1, "_", " ("), ")"),
         Parameter2 = paste0(str_replace(Parameter2, "_", " ("), ")"))

  p <- summary(r) |> 
    plot() +
    theme_minimal()
  list(r = r, plot = p)
}

r <- get_correlation(var = "YONI_")
r$plot
model <- glmmTMB(Confidence ~ Answer / (Condition * YONI_Total) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

get_parameters <- function(model) {
  # Parameters
  params <- parameters::parameters(model, effects = "fixed") 
  # Marginal effects
  at <- c("Answer", "Condition")
  trend <- insight::find_predictors(model)$conditional
  trend <- trend[!trend %in% at]
  marg <- modelbased::estimate_slopes(model, trend = trend, at = at)
  # Output
  list(params = params, marginal_effects = marg)
}

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect <- function(model, var = "YONI_Total", outcome = "Confidence") {
  data <- df |> 
    group_by(Participant, Answer, Condition) |> 
    summarise({{var}} := mean(.data[[var]], na.rm = TRUE),
              SD = sd(.data[[outcome]], na.rm = TRUE),
              {{outcome}} := mean(.data[[outcome]], na.rm = TRUE),
              CI_low = .data[[outcome]] - SD / 2,
              CI_high = .data[[outcome]] + SD / 2) 
  
  dodge_width <- 0.02 * diff(range(data[[var]]))
  ylab <- ifelse(outcome == "RT", "Reaction Time (s)", "Confidence")
  
  link_data <- estimate_relation(model, at = c("Condition", var, "Answer"), length = 30)
  ggplot(link_data, aes_string(x = var, y = "Predicted")) +
    geom_pointrange(data = data, aes_string(y = outcome, color = "Condition", ymin = "CI_low", ymax = "CI_high"), position = position_dodge(width = dodge_width)) +
    geom_ribbon(aes(ymin = CI_low, ymax = CI_high, fill = Condition), alpha = 0.33) + 
    geom_line(aes(color = Condition)) +
    labs(y = ylab, x = paste0(stringr::str_replace(var, "_", " ("), ")")) +
    scale_color_manual(values = c("Polygraph" = "#FF5722", "Social" = "#2196F3")) +
    scale_fill_manual(values = c("Polygraph" = "#FF5722", "Social" = "#2196F3")) +
    facet_wrap(~Answer) 
}

plot_effect(model, var = "YONI_Total", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * YONI_Total) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "YONI_Total", outcome = "RT")
r <- get_correlation(var = "BES_")
r$plot
model <- glmmTMB(Confidence ~ Answer / (Condition * BES_Total) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "BES_Total", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * BES_Total) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "BES_Total", outcome = "RT")
r <- get_correlation(var = "HCT_")
r$plot

# plot(correlation::cor_test(dfsub, "HCT_Accuracy", "LIE_Ability"))
model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Accuracy) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "HCT_Accuracy", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * HCT_Accuracy) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "HCT_Accuracy", outcome = "RT")
model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Confidence) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "HCT_Confidence", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * HCT_Confidence) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "HCT_Confidence", outcome = "RT")
model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Awareness) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "HCT_Awareness", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * HCT_Awareness) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "HCT_Awareness", outcome = "RT")
r <- get_correlation(var = "MAIA_")
r$plot +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_Total) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "MAIA_Total", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * MAIA_Total) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "MAIA_Total", outcome = "RT")
model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_Noticing) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "MAIA_Noticing", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * MAIA_Noticing) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "MAIA_Noticing", outcome = "RT")
model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_NotDistracting) + (1|Participant) + (1|Item), 
                 data = df, family = beta_family())

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "MAIA_NotDistracting", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * MAIA_NotDistracting) + (1|Participant) + (1|Item), 
                 data = df)

results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect(model, var = "MAIA_NotDistracting", outcome = "RT")

Package References

report::cite_packages(sessionInfo())

• Ben-Shachar M, Lüdecke D, Makowski D (2020). effectsize: Estimation of Effect Size Indices and Standardized Parameters. Journal of Open Source Software, 5(56), 2815. doi: 10.21105/joss.02815
• H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
• Hadley Wickham (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0. https://CRAN.R-project.org/package=stringr
• Hadley Wickham (2021). forcats: Tools for Working with Categorical Variables (Factors). R package version 0.5.1. https://CRAN.R-project.org/package=forcats
• Hadley Wickham (2021). tidyr: Tidy Messy Data. R package version 1.1.4. https://CRAN.R-project.org/package=tidyr
• Hadley Wickham and Jim Hester (2021). readr: Read Rectangular Text Data. R package version 2.0.2. https://CRAN.R-project.org/package=readr
• Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7. https://CRAN.R-project.org/package=dplyr
• Kirill Müller and Hadley Wickham (2021). tibble: Simple Data Frames. R package version 3.1.4. https://CRAN.R-project.org/package=tibble
• Lionel Henry and Hadley Wickham (2020). purrr: Functional Programming Tools. R package version 0.3.4. https://CRAN.R-project.org/package=purrr
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References